design1_qubits_analysis_LOM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal import designs, MetalGUI\n",
    "\n",
    "design = designs.DesignPlanar()\n",
    "design.overwrite_enabled = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.chips.main.size_x = '12mm'\n",
    "design.chips.main.size_y = '10mm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gui = MetalGUI(design)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket_cl import TransmonPocketCL\n",
    "\n",
    "design.delete_all_components()\n",
    "\n",
    "design_span_x = 5\n",
    "design_span_y = 3\n",
    "\n",
    "half_chip_width = design_span_x / 2\n",
    "half_chip_height = design_span_y / 2\n",
    "\n",
    "connection_pads_options = dict(\n",
    "    a = dict(loc_W=1, loc_H=-1),\n",
    "    b = dict(loc_W=1, loc_H=1),\n",
    "    c = dict(loc_W=-1, loc_H=-1)\n",
    ")\n",
    "\n",
    "transmons = []\n",
    "\n",
    "transmons.append(TransmonPocketCL(design, 'Q1',\n",
    "                      options=dict(pos_x=f'-{half_chip_width}mm',\n",
    "                                   pos_y=f'{-half_chip_height}mm',\n",
    "                                   connection_pads=dict(**connection_pads_options))))\n",
    "\n",
    "transmons.append(TransmonPocketCL(design, 'Q2',\n",
    "                      options=dict(pos_x=f'0mm',\n",
    "                                   pos_y=f'{half_chip_height}mm',\n",
    "                                   orientation=-90,\n",
    "                                   connection_pads=dict(d=dict(loc_W=-1, loc_H=1), **connection_pads_options))))\n",
    "\n",
    "transmons.append(TransmonPocketCL(design, 'Q3',\n",
    "                      options=dict(pos_x=f'0mm',\n",
    "                                   pos_y=f'{-half_chip_height}mm',\n",
    "                                   orientation=90,\n",
    "                                   connection_pads=dict(d=dict(loc_W=-1, loc_H=1), **connection_pads_options))))\n",
    "\n",
    "transmons.append(TransmonPocketCL(design, 'Q4',\n",
    "                      options=dict(pos_x=f'{half_chip_width}mm',\n",
    "                                   pos_y=f'{half_chip_height}mm',\n",
    "                                   orientation=180,\n",
    "                                   connection_pads=dict(**connection_pads_options))))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal import Dict\n",
    "\n",
    "fillet='99.99um'\n",
    "options = Dict(\n",
    "    meander=Dict(\n",
    "        lead_start='0.1mm',\n",
    "        lead_end='0.1mm',\n",
    "        asymmetry='0 um')\n",
    ")\n",
    "\n",
    "def connect(component_name: str, component1: str, pin1: str, component2: str, pin2: str,\n",
    "            length: str,\n",
    "            asymmetry='0 um', start_strght='0 um', end_strght='0 um', flip=False):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=component1,\n",
    "                pin=pin1),\n",
    "            end_pin=Dict(\n",
    "                component=component2,\n",
    "                pin=pin2)),\n",
    "        lead=Dict(\n",
    "            start_straight=start_strght,\n",
    "            end_straight=end_strght\n",
    "        ),\n",
    "        total_length=length,\n",
    "    fillet = '99.9um')\n",
    "    myoptions.update(options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    myoptions.meander.lead_direction_inverted = 'true' if flip else 'false'\n",
    "    return RouteMeander(design, component_name, myoptions)\n",
    "\n",
    "asym_h = 100\n",
    "asym_v = 100\n",
    "cpw = []\n",
    "\n",
    "cpw.append(connect('cpw1', 'Q1', 'b', 'Q2', 'a', '8 mm', f'+{asym_h}um', '0.1mm', '0.1mm'))\n",
    "cpw.append(connect('cpw2', 'Q3', 'b', 'Q2', 'b', '8 mm', f'-{asym_v}um', '0.6mm', '0.4mm'))\n",
    "cpw.append(connect('cpw3', 'Q4', 'b', 'Q3', 'a', '8 mm', f'+{asym_h}um', '0.1mm', '0.1mm'))\n",
    "cpw.append(connect('cpw4', 'Q3', 'd', 'Q1', 'a', '8 mm', f'-{asym_h}um', '0.1mm', '0.1mm'))\n",
    "cpw.append(connect('cpw5', 'Q2', 'd', 'Q4', 'a', '8 mm', f'-{asym_h}um', '0.1mm', '0.1mm'))\n",
    "\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.terminations.launchpad_wb_coupled import LaunchpadWirebondCoupled\n",
    "\n",
    "readouts_lwc = []\n",
    "control_lwc = []\n",
    "\n",
    "offset_x = 0\n",
    "offset_y = 1\n",
    "\n",
    "\n",
    "#Readouts\n",
    "readouts_lwc.append(LaunchpadWirebondCoupled(design, 'R1',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '-5mm',\n",
    "                                         pos_y = f'-{half_chip_height+offset_y}mm',\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "readouts_lwc.append(LaunchpadWirebondCoupled(design, 'R2',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '-1mm',\n",
    "                                         pos_y = '4mm',\n",
    "                                         orientation = -90,\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "readouts_lwc.append(LaunchpadWirebondCoupled(design, 'R3',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '1mm',\n",
    "                                         pos_y = '-4mm',\n",
    "                                         orientation = 90,\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "readouts_lwc.append(LaunchpadWirebondCoupled(design, 'R4',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '5mm',\n",
    "                                         pos_y = f'{half_chip_height+offset_y}mm',\n",
    "                                         orientation = 180,\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "#Controls\n",
    "control_lwc.append(LaunchpadWirebondCoupled(design, 'CL1',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '-5mm',\n",
    "                                         pos_y = '2mm',\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "control_lwc.append(LaunchpadWirebondCoupled(design, 'CL2',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '4mm',\n",
    "                                         pos_y = '4mm',\n",
    "                                         orientation = -90,\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "control_lwc.append(LaunchpadWirebondCoupled(design, 'CL3',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '-4mm',\n",
    "                                         pos_y = '-4mm',\n",
    "                                         orientation = 90,\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "control_lwc.append(LaunchpadWirebondCoupled(design, 'CL4',\n",
    "                                         options = dict(\n",
    "                                         pos_x = '5mm',\n",
    "                                         pos_y = '-2mm',\n",
    "                                         orientation = 180,\n",
    "                                         lead_length = '30um')))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "readout_lines = []\n",
    "\n",
    "asym_14 = 700\n",
    "asym_23 = 700\n",
    "options = Dict(\n",
    "    lead=Dict(\n",
    "        start_straight='430um',\n",
    "        end_straight='0um'),\n",
    "    fillet='99.99um')\n",
    "\n",
    "readout_lines.append(connect('ol1', 'Q1', 'c', 'R1', 'tie', '8 mm', f'{asym_14}um'))\n",
    "readout_lines.append(connect('ol2', 'Q2', 'c', 'R2', 'tie', '8 mm', f'{asym_23}um'))\n",
    "readout_lines.append(connect('ol3', 'Q3', 'c', 'R3', 'tie', '8 mm', f'{asym_23}um'))\n",
    "readout_lines.append(connect('ol4', 'Q4', 'c', 'R4', 'tie', '8 mm', f'{asym_14}um'))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.tlines.anchored_path import RouteAnchors\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "\n",
    "control_lines = []\n",
    "\n",
    "def connectRouteAnchor(name: str,\n",
    "                       component1: str, pin1: str, component2: str, pin2: str,\n",
    "                       anchor_points: OrderedDict) -> RouteAnchors:\n",
    "\n",
    "    options_line_cl = dict(\n",
    "        pin_inputs = dict(start_pin = dict(component = component1, pin = pin1),\n",
    "                          end_pin = dict(component = component2, pin = pin2)),\n",
    "        anchors = anchor_points,\n",
    "        lead = dict(start_straight = '200um',\n",
    "                    end_straight = '225um'),\n",
    "        fillet = fillet\n",
    "    )\n",
    "    \n",
    "    return RouteAnchors(design, name, options_line_cl)\n",
    "\n",
    "\n",
    "\n",
    "anchors1c = OrderedDict()\n",
    "anchors1c[0] = np.array([-4, -1.42])\n",
    "anchors1c[1] = np.array([-4, 2])\n",
    "\n",
    "control_lines.append(connectRouteAnchor('line_cl1', 'Q1', 'Charge_Line', 'CL1', 'tie', anchors1c))\n",
    "\n",
    "\n",
    "anchors2c = OrderedDict()\n",
    "anchors2c[0] = np.array([0.08, 3.25])\n",
    "anchors2c[1] = np.array([4, 3.25])\n",
    "\n",
    "control_lines.append(connectRouteAnchor('line_cl2', 'Q2', 'Charge_Line', 'CL2', 'tie', anchors2c))\n",
    "\n",
    "anchors3c = OrderedDict()\n",
    "anchors3c[0] = np.array([-0.08, -3.25])\n",
    "anchors3c[1] = np.array([-4, -3.25])\n",
    "\n",
    "control_lines.append(connectRouteAnchor('line_cl3', 'Q3', 'Charge_Line', 'CL3', 'tie', anchors3c))\n",
    "\n",
    "anchors4c = OrderedDict()\n",
    "anchors4c[0] = np.array([4, 1.42])\n",
    "anchors4c[1] = np.array([4, -2])\n",
    "\n",
    "control_lines.append(connectRouteAnchor('line_cl4', 'Q4', 'Charge_Line', 'CL4', 'tie', anchors4c))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Qubit Frequencies\n",
    "# Q1 : 5\n",
    "# Q2 : 5.1\n",
    "# Q3 : 5.2\n",
    "# Q4 : 5.3\n",
    "\n",
    "# Readout Frequencies\n",
    "# R1 : 7\n",
    "# R2 : 7.1\n",
    "# R3 : 7.2\n",
    "# R4 : 7.3\n",
    "\n",
    "# CPW Frequencies\n",
    "# cpw1 : 7.5\n",
    "# cpw2 : 7.6\n",
    "# cpw3 : 7.7\n",
    "# cpw4 : 7.8\n",
    "# cpw5 : 7.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Old LOM Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Qubit (Q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:10AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 12:10AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 12:10AM [load_ansys_project]: \tOpened Ansys Desktop v2021.1.0\n",
      "INFO 12:10AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Users/Nilay/Documents/Ansoft/\n",
      "\tProject:   Project3\n",
      "INFO 12:10AM [connect_design]: No active design found (or error getting active design).\n",
      "INFO 12:10AM [connect]: \t Connected to project \"Project3\". No design detected\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c1 = LOManalysis(design, \"q3d\")\n",
    "q3d1 = c1.sim.renderer\n",
    "q3d1.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:10AM 48s WARNING [activate_ansys_design]: The design_name=Tune_Q1 was not in active project.  Designs in active project are: \n",
      "[].  A new design will be added to the project.  \n",
      "INFO 12:10AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q1 [Solution type: Q3D]\n",
      "WARNING 12:10AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 12:10AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 12:10AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n"
     ]
    }
   ],
   "source": [
    "transmons[0].options.pad_gap    = '40um'\n",
    "transmons[0].options.pad_width  = '405um'\n",
    "transmons[0].options.pad_height = '90um'\n",
    "gui.rebuild() # IMPORTANT\n",
    "\n",
    "q3d1.activate_ansys_design(\"Tune_Q1\", 'capacitive')\n",
    "q3d1.render_design(['Q1'], [('Q1', 'c'), ('Q1', 'a'),('Q1', 'b'),('Q1', 'Charge_Line')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#q3d1.save_screenshot(\"C:/Users/Nilay/Documents/GitHub/qiskit-metal-qubit-design/q1.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:11AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:11AM [analyze]: Analyzing setup Setup\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpk4noiicm.txt, C, , Setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp2rsscazl.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpat6fcq_8.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpzzq7zm2f.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp1u2ifj38.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpdr04tbch.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp9ngnh4__.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpwejnq414.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpn0bfuzp5.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmphl53ca4p.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpz2e_53l1.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n",
      "INFO 12:14AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmphf7hbh7e.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 11, False\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a_connector_pad_Q1</th>\n",
       "      <th>b_connector_pad_Q1</th>\n",
       "      <th>c_connector_pad_Q1</th>\n",
       "      <th>cl_metal_Q1</th>\n",
       "      <th>ground_main_plane</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a_connector_pad_Q1</th>\n",
       "      <td>50.69228</td>\n",
       "      <td>-0.33796</td>\n",
       "      <td>-0.45073</td>\n",
       "      <td>-0.00931</td>\n",
       "      <td>-34.00654</td>\n",
       "      <td>-13.49711</td>\n",
       "      <td>-1.52332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_connector_pad_Q1</th>\n",
       "      <td>-0.33796</td>\n",
       "      <td>50.74702</td>\n",
       "      <td>-0.14421</td>\n",
       "      <td>-0.01141</td>\n",
       "      <td>-34.18666</td>\n",
       "      <td>-1.49626</td>\n",
       "      <td>-13.69033</td>\n",
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       "    <tr>\n",
       "      <th>c_connector_pad_Q1</th>\n",
       "      <td>-0.45073</td>\n",
       "      <td>-0.14421</td>\n",
       "      <td>50.73449</td>\n",
       "      <td>-0.05230</td>\n",
       "      <td>-34.12539</td>\n",
       "      <td>-13.50667</td>\n",
       "      <td>-1.58640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cl_metal_Q1</th>\n",
       "      <td>-0.00931</td>\n",
       "      <td>-0.01141</td>\n",
       "      <td>-0.05230</td>\n",
       "      <td>16.01295</td>\n",
       "      <td>-15.53175</td>\n",
       "      <td>-0.12294</td>\n",
       "      <td>-0.20944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-34.00654</td>\n",
       "      <td>-34.18666</td>\n",
       "      <td>-34.12539</td>\n",
       "      <td>-15.53175</td>\n",
       "      <td>248.09927</td>\n",
       "      <td>-33.23860</td>\n",
       "      <td>-37.76618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q1</th>\n",
       "      <td>-13.49711</td>\n",
       "      <td>-1.49626</td>\n",
       "      <td>-13.50667</td>\n",
       "      <td>-0.12294</td>\n",
       "      <td>-33.23860</td>\n",
       "      <td>90.53020</td>\n",
       "      <td>-26.22066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q1</th>\n",
       "      <td>-1.52332</td>\n",
       "      <td>-13.69033</td>\n",
       "      <td>-1.58640</td>\n",
       "      <td>-0.20944</td>\n",
       "      <td>-37.76618</td>\n",
       "      <td>-26.22066</td>\n",
       "      <td>83.73712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    a_connector_pad_Q1  b_connector_pad_Q1  \\\n",
       "a_connector_pad_Q1            50.69228            -0.33796   \n",
       "b_connector_pad_Q1            -0.33796            50.74702   \n",
       "c_connector_pad_Q1            -0.45073            -0.14421   \n",
       "cl_metal_Q1                   -0.00931            -0.01141   \n",
       "ground_main_plane            -34.00654           -34.18666   \n",
       "pad_bot_Q1                   -13.49711            -1.49626   \n",
       "pad_top_Q1                    -1.52332           -13.69033   \n",
       "\n",
       "                    c_connector_pad_Q1  cl_metal_Q1  ground_main_plane  \\\n",
       "a_connector_pad_Q1            -0.45073     -0.00931          -34.00654   \n",
       "b_connector_pad_Q1            -0.14421     -0.01141          -34.18666   \n",
       "c_connector_pad_Q1            50.73449     -0.05230          -34.12539   \n",
       "cl_metal_Q1                   -0.05230     16.01295          -15.53175   \n",
       "ground_main_plane            -34.12539    -15.53175          248.09927   \n",
       "pad_bot_Q1                   -13.50667     -0.12294          -33.23860   \n",
       "pad_top_Q1                    -1.58640     -0.20944          -37.76618   \n",
       "\n",
       "                    pad_bot_Q1  pad_top_Q1  \n",
       "a_connector_pad_Q1   -13.49711    -1.52332  \n",
       "b_connector_pad_Q1    -1.49626   -13.69033  \n",
       "c_connector_pad_Q1   -13.50667    -1.58640  \n",
       "cl_metal_Q1           -0.12294    -0.20944  \n",
       "ground_main_plane    -33.23860   -37.76618  \n",
       "pad_bot_Q1            90.53020   -26.22066  \n",
       "pad_top_Q1           -26.22066    83.73712  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3d1.add_q3d_setup(name=\"Setup\", max_passes=15, min_converged_passes=5,percent_error=0.05 )\n",
    "q3d1.analyze_setup(\"Setup\")\n",
    "c1.sim.capacitance_matrix, c1.sim.units = q3d1.get_capacitance_matrix()\n",
    "c1.sim.capacitance_all_passes, _ = q3d1.get_capacitance_all_passes()\n",
    "c1.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5] [6 0 1 2]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.038986 [GHz]\n",
      "EC 258.992882 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -296.183928 [MHz]\n",
      "dispersion 6.554938 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 74.790581 [fF]\n",
      "T1 103.960575 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 -9.546382 [fF]\n",
      "gbus1_in_MHz -133.797759 [MHz]\n",
      "χ_bus1 -1.631893 [MHz]\n",
      "1/T1bus1 1432.030599 [Hz]\n",
      "T1bus1 111.139345 [us]\n",
      "\n",
      "tCqbus2 -1.118173 [fF]\n",
      "gbus2_in_MHz -16.416498 [MHz]\n",
      "χ_bus2 -0.019904 [MHz]\n",
      "1/T1bus2 1.699039 [Hz]\n",
      "T1bus2 93673.510356 [us]\n",
      "\n",
      "tCqbus3 7.802323 [fF]\n",
      "gbus3_in_MHz 110.022103 [MHz]\n",
      "χ_bus3 -1.103453 [MHz]\n",
      "1/T1bus3 96.831019 [Hz]\n",
      "T1bus3 1643.635945 [us]\n",
      "\n",
      "tCqbus4 -1.094271 [fF]\n",
      "gbus4_in_MHz -30.499829 [MHz]\n",
      "χ_bus4 -0.006786 [MHz]\n",
      "1/T1bus4 0.355692 [Hz]\n",
      "T1bus4 447451.877124 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 51.321602 [MHz]\n",
      "gbus1_3 75.509387 [MHz]\n",
      "gbus1_4 94.337324 [MHz]\n",
      "gbus2_3 27.641214 [MHz]\n",
      "gbus2_4 70.397455 [MHz]\n",
      "gbus3_4 49.451453 [MHz]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.350344</td>\n",
       "      <td>294.229448</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-340.311696</td>\n",
       "      <td>22.275128</td>\n",
       "      <td>[-128.35246654158456, -9.17941931140656, 104.1...</td>\n",
       "      <td>[-2.164597524286147, -0.008731817658863017, -1...</td>\n",
       "      <td>2.164598</td>\n",
       "      <td>128.352467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.26148</td>\n",
       "      <td>283.911694</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-327.299719</td>\n",
       "      <td>15.948383</td>\n",
       "      <td>[-114.66675420681587, -20.40185276740615, 101....</td>\n",
       "      <td>[-1.5527649390243978, -0.039091033617974426, -...</td>\n",
       "      <td>1.552765</td>\n",
       "      <td>114.666754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.176298</td>\n",
       "      <td>274.217913</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-315.143293</td>\n",
       "      <td>11.451606</td>\n",
       "      <td>[-119.39061885708729, -19.083278503181756, 102...</td>\n",
       "      <td>[-1.5224926220627786, -0.031165289247393264, -...</td>\n",
       "      <td>1.522493</td>\n",
       "      <td>119.390619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.129184</td>\n",
       "      <td>268.938373</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-308.550373</td>\n",
       "      <td>9.488904</td>\n",
       "      <td>[-122.03195269527339, -17.271945932345773, 104...</td>\n",
       "      <td>[-1.5057426680461385, -0.02426326498939728, -1...</td>\n",
       "      <td>1.505743</td>\n",
       "      <td>122.031953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.103636</td>\n",
       "      <td>266.09978</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-305.013728</td>\n",
       "      <td>8.556553</td>\n",
       "      <td>[-126.54265266631532, -17.323860499727104, 106...</td>\n",
       "      <td>[-1.5720042552275753, -0.023748609155416878, -...</td>\n",
       "      <td>1.572004</td>\n",
       "      <td>126.542653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.078283</td>\n",
       "      <td>263.299787</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-301.530715</td>\n",
       "      <td>7.713856</td>\n",
       "      <td>[-128.20326305490042, -16.6454432215244, 107.6...</td>\n",
       "      <td>[-1.567154158609593, -0.021338157443123923, -1...</td>\n",
       "      <td>1.567154</td>\n",
       "      <td>128.203263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.06773</td>\n",
       "      <td>262.139281</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-300.088732</td>\n",
       "      <td>7.385752</td>\n",
       "      <td>[-130.4059493987157, -16.39335714540411, 108.2...</td>\n",
       "      <td>[-1.6019660204849244, -0.02046493257837152, -1...</td>\n",
       "      <td>1.601966</td>\n",
       "      <td>130.405949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.053504</td>\n",
       "      <td>260.579337</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-298.151914</td>\n",
       "      <td>6.963364</td>\n",
       "      <td>[-131.6973884653301, -16.351725633271204, 108....</td>\n",
       "      <td>[-1.6074733535501131, -0.02005481038915058, -1...</td>\n",
       "      <td>1.607473</td>\n",
       "      <td>131.697388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.043095</td>\n",
       "      <td>259.441355</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-296.740077</td>\n",
       "      <td>6.668284</td>\n",
       "      <td>[-132.96599108673794, -16.089255234233224, 109...</td>\n",
       "      <td>[-1.6192347493361468, -0.01920238066046346, -1...</td>\n",
       "      <td>1.619235</td>\n",
       "      <td>132.965991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.038986</td>\n",
       "      <td>258.992882</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-296.183928</td>\n",
       "      <td>6.554938</td>\n",
       "      <td>[-133.79775901970703, -16.4164982204082, 110.0...</td>\n",
       "      <td>[-1.6318925819901084, -0.019904349916913644, -...</td>\n",
       "      <td>1.631893</td>\n",
       "      <td>133.797759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fQ          EC       EJ       alpha dispersion  \\\n",
       "1   5.350344  294.229448  13.6163 -340.311696  22.275128   \n",
       "2    5.26148  283.911694  13.6163 -327.299719  15.948383   \n",
       "3   5.176298  274.217913  13.6163 -315.143293  11.451606   \n",
       "4   5.129184  268.938373  13.6163 -308.550373   9.488904   \n",
       "5   5.103636   266.09978  13.6163 -305.013728   8.556553   \n",
       "6   5.078283  263.299787  13.6163 -301.530715   7.713856   \n",
       "7    5.06773  262.139281  13.6163 -300.088732   7.385752   \n",
       "8   5.053504  260.579337  13.6163 -298.151914   6.963364   \n",
       "9   5.043095  259.441355  13.6163 -296.740077   6.668284   \n",
       "10  5.038986  258.992882  13.6163 -296.183928   6.554938   \n",
       "\n",
       "                                                 gbus  \\\n",
       "1   [-128.35246654158456, -9.17941931140656, 104.1...   \n",
       "2   [-114.66675420681587, -20.40185276740615, 101....   \n",
       "3   [-119.39061885708729, -19.083278503181756, 102...   \n",
       "4   [-122.03195269527339, -17.271945932345773, 104...   \n",
       "5   [-126.54265266631532, -17.323860499727104, 106...   \n",
       "6   [-128.20326305490042, -16.6454432215244, 107.6...   \n",
       "7   [-130.4059493987157, -16.39335714540411, 108.2...   \n",
       "8   [-131.6973884653301, -16.351725633271204, 108....   \n",
       "9   [-132.96599108673794, -16.089255234233224, 109...   \n",
       "10  [-133.79775901970703, -16.4164982204082, 110.0...   \n",
       "\n",
       "                                           chi_in_MHz    χr MHz      gr MHz  \n",
       "1   [-2.164597524286147, -0.008731817658863017, -1...  2.164598  128.352467  \n",
       "2   [-1.5527649390243978, -0.039091033617974426, -...  1.552765  114.666754  \n",
       "3   [-1.5224926220627786, -0.031165289247393264, -...  1.522493  119.390619  \n",
       "4   [-1.5057426680461385, -0.02426326498939728, -1...  1.505743  122.031953  \n",
       "5   [-1.5720042552275753, -0.023748609155416878, -...  1.572004  126.542653  \n",
       "6   [-1.567154158609593, -0.021338157443123923, -1...  1.567154  128.203263  \n",
       "7   [-1.6019660204849244, -0.02046493257837152, -1...  1.601966  130.405949  \n",
       "8   [-1.6074733535501131, -0.02005481038915058, -1...  1.607473  131.697388  \n",
       "9   [-1.6192347493361468, -0.01920238066046346, -1...  1.619235  132.965991  \n",
       "10  [-1.6318925819901084, -0.019904349916913644, -...  1.631893  133.797759  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1.setup.junctions = Dict(Lj=12, Cj=1)\n",
    "c1.setup.freq_readout = 7.5\n",
    "c1.setup.freq_bus = [7.8, 7.5, 15]\n",
    "\n",
    "c1.run_lom()\n",
    "c1.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Tune_Q1\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:14AM [hfss_report_full_convergence]: Creating report for variation 0\n"
     ]
    }
   ],
   "source": [
    "c1.plot_convergence()\n",
    "c1.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./new graphs/q1 fig1.png\">\n",
    "<img src=\"./new graphs/q1 fig2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Qubit (Q2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:14AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 12:14AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 12:14AM [load_ansys_project]: \tOpened Ansys Desktop v2021.1.0\n",
      "INFO 12:14AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Users/Nilay/Documents/Ansoft/\n",
      "\tProject:   Project3\n",
      "INFO 12:14AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q1 [Solution type: Q3D]\n",
      "INFO 12:14AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:14AM [connect]: \tConnected to project \"Project3\" and design \"Tune_Q1\" 😀 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c2 = LOManalysis(design, \"q3d\")\n",
    "q3d2 = c2.sim.renderer\n",
    "q3d2.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:14AM 44s WARNING [activate_ansys_design]: The design_name=Tune_Q2 was not in active project.  Designs in active project are: \n",
      "['Tune_Q1'].  A new design will be added to the project.  \n",
      "INFO 12:14AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q2 [Solution type: Q3D]\n",
      "WARNING 12:14AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 12:14AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 12:14AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n"
     ]
    }
   ],
   "source": [
    "transmons[1].options.pad_gap    = '40um'\n",
    "transmons[1].options.pad_width  = '378um'\n",
    "transmons[1].options.pad_height = '90um'\n",
    "gui.rebuild()\n",
    "\n",
    "\n",
    "q3d2.activate_ansys_design(\"Tune_Q2\", 'capacitive')\n",
    "q3d2.render_design(['Q2'], [('Q2', 'c'), ('Q2', 'a'),('Q2', 'b'),('Q2', 'd'),('Q2', 'Charge_Line')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#q3d2.save_screenshot(path=\"C:/Users/Nilay/Documents/GitHub/qiskit-metal-qubit-design/q2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:14AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:14AM [analyze]: Analyzing setup Setup\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmply9880q_.txt, C, , Setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmprnuh6m51.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpgff_e2vk.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpstx71gkr.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpn6ms1h14.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpihj09t3i.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpoa9t2xc_.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp_0_sarqj.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp3c3gtw92.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp0s5lyq2f.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp0yh6kazd.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n",
      "INFO 12:17AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp0qfaz54b.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 11, False\n"
     ]
    },
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a_connector_pad_Q2</th>\n",
       "      <th>b_connector_pad_Q2</th>\n",
       "      <th>c_connector_pad_Q2</th>\n",
       "      <th>cl_metal_Q2</th>\n",
       "      <th>d_connector_pad_Q2</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q2</th>\n",
       "      <th>pad_top_Q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a_connector_pad_Q2</th>\n",
       "      <td>51.40952</td>\n",
       "      <td>-0.37654</td>\n",
       "      <td>-0.58575</td>\n",
       "      <td>-0.00974</td>\n",
       "      <td>-0.16204</td>\n",
       "      <td>-34.36968</td>\n",
       "      <td>-13.50009</td>\n",
       "      <td>-1.49803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_connector_pad_Q2</th>\n",
       "      <td>-0.37654</td>\n",
       "      <td>51.41744</td>\n",
       "      <td>-0.16200</td>\n",
       "      <td>-0.01118</td>\n",
       "      <td>-0.58254</td>\n",
       "      <td>-34.36426</td>\n",
       "      <td>-1.50474</td>\n",
       "      <td>-13.50438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_connector_pad_Q2</th>\n",
       "      <td>-0.58575</td>\n",
       "      <td>-0.16200</td>\n",
       "      <td>51.38463</td>\n",
       "      <td>-0.05370</td>\n",
       "      <td>-0.37605</td>\n",
       "      <td>-34.31367</td>\n",
       "      <td>-13.49286</td>\n",
       "      <td>-1.49703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cl_metal_Q2</th>\n",
       "      <td>-0.00974</td>\n",
       "      <td>-0.01118</td>\n",
       "      <td>-0.05370</td>\n",
       "      <td>15.94377</td>\n",
       "      <td>-0.15415</td>\n",
       "      <td>-15.38032</td>\n",
       "      <td>-0.10835</td>\n",
       "      <td>-0.15121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_connector_pad_Q2</th>\n",
       "      <td>-0.16204</td>\n",
       "      <td>-0.58254</td>\n",
       "      <td>-0.37605</td>\n",
       "      <td>-0.15415</td>\n",
       "      <td>51.48480</td>\n",
       "      <td>-34.28866</td>\n",
       "      <td>-1.50442</td>\n",
       "      <td>-13.50438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-34.36968</td>\n",
       "      <td>-34.36426</td>\n",
       "      <td>-34.31367</td>\n",
       "      <td>-15.38032</td>\n",
       "      <td>-34.28866</td>\n",
       "      <td>271.68302</td>\n",
       "      <td>-30.07801</td>\n",
       "      <td>-30.02652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q2</th>\n",
       "      <td>-13.50009</td>\n",
       "      <td>-1.50474</td>\n",
       "      <td>-13.49286</td>\n",
       "      <td>-0.10835</td>\n",
       "      <td>-1.50442</td>\n",
       "      <td>-30.07801</td>\n",
       "      <td>86.23568</td>\n",
       "      <td>-23.77326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q2</th>\n",
       "      <td>-1.49803</td>\n",
       "      <td>-13.50438</td>\n",
       "      <td>-1.49703</td>\n",
       "      <td>-0.15121</td>\n",
       "      <td>-13.50438</td>\n",
       "      <td>-30.02652</td>\n",
       "      <td>-23.77326</td>\n",
       "      <td>86.25688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    a_connector_pad_Q2  b_connector_pad_Q2  \\\n",
       "a_connector_pad_Q2            51.40952            -0.37654   \n",
       "b_connector_pad_Q2            -0.37654            51.41744   \n",
       "c_connector_pad_Q2            -0.58575            -0.16200   \n",
       "cl_metal_Q2                   -0.00974            -0.01118   \n",
       "d_connector_pad_Q2            -0.16204            -0.58254   \n",
       "ground_main_plane            -34.36968           -34.36426   \n",
       "pad_bot_Q2                   -13.50009            -1.50474   \n",
       "pad_top_Q2                    -1.49803           -13.50438   \n",
       "\n",
       "                    c_connector_pad_Q2  cl_metal_Q2  d_connector_pad_Q2  \\\n",
       "a_connector_pad_Q2            -0.58575     -0.00974            -0.16204   \n",
       "b_connector_pad_Q2            -0.16200     -0.01118            -0.58254   \n",
       "c_connector_pad_Q2            51.38463     -0.05370            -0.37605   \n",
       "cl_metal_Q2                   -0.05370     15.94377            -0.15415   \n",
       "d_connector_pad_Q2            -0.37605     -0.15415            51.48480   \n",
       "ground_main_plane            -34.31367    -15.38032           -34.28866   \n",
       "pad_bot_Q2                   -13.49286     -0.10835            -1.50442   \n",
       "pad_top_Q2                    -1.49703     -0.15121           -13.50438   \n",
       "\n",
       "                    ground_main_plane  pad_bot_Q2  pad_top_Q2  \n",
       "a_connector_pad_Q2          -34.36968   -13.50009    -1.49803  \n",
       "b_connector_pad_Q2          -34.36426    -1.50474   -13.50438  \n",
       "c_connector_pad_Q2          -34.31367   -13.49286    -1.49703  \n",
       "cl_metal_Q2                 -15.38032    -0.10835    -0.15121  \n",
       "d_connector_pad_Q2          -34.28866    -1.50442   -13.50438  \n",
       "ground_main_plane           271.68302   -30.07801   -30.02652  \n",
       "pad_bot_Q2                  -30.07801    86.23568   -23.77326  \n",
       "pad_top_Q2                  -30.02652   -23.77326    86.25688  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3d2.add_q3d_setup(name=\"Setup\", max_passes=15, min_converged_passes=5,percent_error=0.05 )\n",
    "q3d2.analyze_setup(\"Setup\")\n",
    "c2.sim.capacitance_matrix, c2.sim.units = q3d2.get_capacitance_matrix()\n",
    "c2.sim.capacitance_all_passes, _ = q3d2.get_capacitance_all_passes()\n",
    "c2.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 6] [7 0 1 2 3]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.106990 [GHz]\n",
      "EC 266.471547 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -305.476596 [MHz]\n",
      "dispersion 8.674073 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 72.691544 [fF]\n",
      "T1 50.242131 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 -11.522188 [fF]\n",
      "gbus1_in_MHz -156.572634 [MHz]\n",
      "χ_bus1 -3.372646 [MHz]\n",
      "1/T1bus1 3067.920893 [Hz]\n",
      "T1bus1 51.877134 [us]\n",
      "\n",
      "tCqbus2 -2.599376 [fF]\n",
      "gbus2_in_MHz -37.501606 [MHz]\n",
      "χ_bus2 -0.138599 [MHz]\n",
      "1/T1bus2 12.090363 [Hz]\n",
      "T1bus2 13163.784879 [us]\n",
      "\n",
      "tCqbus3 6.663206 [fF]\n",
      "gbus3_in_MHz 97.261147 [MHz]\n",
      "χ_bus3 -0.865074 [MHz]\n",
      "1/T1bus3 74.739319 [Hz]\n",
      "T1bus3 2129.467395 [us]\n",
      "\n",
      "tCqbus4 -2.606547 [fF]\n",
      "gbus4_in_MHz -39.583439 [MHz]\n",
      "χ_bus4 -0.116409 [MHz]\n",
      "1/T1bus4 9.784660 [Hz]\n",
      "T1bus4 16265.761983 [us]\n",
      "\n",
      "tCqbus5 3.418668 [fF]\n",
      "gbus5_in_MHz 112.087448 [MHz]\n",
      "χ_bus5 -0.068834 [MHz]\n",
      "1/T1bus5 3.223370 [Hz]\n",
      "T1bus5 49375.323804 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 36.540741 [MHz]\n",
      "gbus1_3 64.370996 [MHz]\n",
      "gbus1_4 38.336201 [MHz]\n",
      "gbus1_5 24.825540 [MHz]\n",
      "gbus2_3 24.452184 [MHz]\n",
      "gbus2_4 34.275119 [MHz]\n",
      "gbus2_5 22.901242 [MHz]\n",
      "gbus3_4 24.931352 [MHz]\n",
      "gbus3_5 17.309203 [MHz]\n",
      "gbus4_5 24.361167 [MHz]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
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       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.423998</td>\n",
       "      <td>302.941124</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-351.35719</td>\n",
       "      <td>29.135515</td>\n",
       "      <td>[-149.7938927198854, -32.369160005846624, 93.5...</td>\n",
       "      <td>[-4.743896200332651, -0.15064122171714955, -1....</td>\n",
       "      <td>4.743896</td>\n",
       "      <td>149.793893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.337243</td>\n",
       "      <td>292.695178</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-338.372006</td>\n",
       "      <td>21.219633</td>\n",
       "      <td>[-140.3693717002468, -35.204483730520856, 90.5...</td>\n",
       "      <td>[-3.688792537026435, -0.16027905223925185, -0....</td>\n",
       "      <td>3.688793</td>\n",
       "      <td>140.369372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.260706</td>\n",
       "      <td>283.822817</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-327.187962</td>\n",
       "      <td>15.901273</td>\n",
       "      <td>[-145.22964750794355, -34.1761096455683, 91.30...</td>\n",
       "      <td>[-3.5564231159948405, -0.13780865504675055, -0...</td>\n",
       "      <td>3.556423</td>\n",
       "      <td>145.229648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.219784</td>\n",
       "      <td>279.142774</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-321.310996</td>\n",
       "      <td>13.580019</td>\n",
       "      <td>[-146.68547873216784, -35.273931876923136, 92....</td>\n",
       "      <td>[-3.4339388654046306, -0.13986054119450542, -0...</td>\n",
       "      <td>3.433939</td>\n",
       "      <td>146.685479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.183758</td>\n",
       "      <td>275.059218</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-316.1957</td>\n",
       "      <td>11.793873</td>\n",
       "      <td>[-149.79056376648444, -34.764690085456614, 93....</td>\n",
       "      <td>[-3.4133645521411466, -0.13022118096526072, -0...</td>\n",
       "      <td>3.413365</td>\n",
       "      <td>149.790564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.154232</td>\n",
       "      <td>271.738019</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-312.04404</td>\n",
       "      <td>10.490952</td>\n",
       "      <td>[-151.56931316731388, -36.27718223357999, 94.9...</td>\n",
       "      <td>[-3.3615173453262583, -0.13699457048810307, -0...</td>\n",
       "      <td>3.361517</td>\n",
       "      <td>151.569313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.138026</td>\n",
       "      <td>269.924793</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-309.780694</td>\n",
       "      <td>9.832324</td>\n",
       "      <td>[-152.88891297935538, -36.769173225736196, 95....</td>\n",
       "      <td>[-3.3484480460203634, -0.1381096261830813, -0....</td>\n",
       "      <td>3.348448</td>\n",
       "      <td>152.888913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.124563</td>\n",
       "      <td>268.42364</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-307.90864</td>\n",
       "      <td>9.313755</td>\n",
       "      <td>[-154.18196982402736, -37.053771360193124, 96....</td>\n",
       "      <td>[-3.3460100166051583, -0.1380842949746457, -0....</td>\n",
       "      <td>3.346010</td>\n",
       "      <td>154.181970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.114246</td>\n",
       "      <td>267.276553</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-306.479201</td>\n",
       "      <td>8.93322</td>\n",
       "      <td>[-155.92347754054478, -37.392301324447665, 96....</td>\n",
       "      <td>[-3.3763925120509524, -0.138951259968651, -0.8...</td>\n",
       "      <td>3.376393</td>\n",
       "      <td>155.923478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.10699</td>\n",
       "      <td>266.471547</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-305.476596</td>\n",
       "      <td>8.674073</td>\n",
       "      <td>[-156.57263382288738, -37.50160617150344, 97.2...</td>\n",
       "      <td>[-3.372645810154595, -0.13859898335873305, -0....</td>\n",
       "      <td>3.372646</td>\n",
       "      <td>156.572634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fQ          EC       EJ       alpha dispersion  \\\n",
       "1   5.423998  302.941124  13.6163  -351.35719  29.135515   \n",
       "2   5.337243  292.695178  13.6163 -338.372006  21.219633   \n",
       "3   5.260706  283.822817  13.6163 -327.187962  15.901273   \n",
       "4   5.219784  279.142774  13.6163 -321.310996  13.580019   \n",
       "5   5.183758  275.059218  13.6163   -316.1957  11.793873   \n",
       "6   5.154232  271.738019  13.6163  -312.04404  10.490952   \n",
       "7   5.138026  269.924793  13.6163 -309.780694   9.832324   \n",
       "8   5.124563   268.42364  13.6163  -307.90864   9.313755   \n",
       "9   5.114246  267.276553  13.6163 -306.479201    8.93322   \n",
       "10   5.10699  266.471547  13.6163 -305.476596   8.674073   \n",
       "\n",
       "                                                 gbus  \\\n",
       "1   [-149.7938927198854, -32.369160005846624, 93.5...   \n",
       "2   [-140.3693717002468, -35.204483730520856, 90.5...   \n",
       "3   [-145.22964750794355, -34.1761096455683, 91.30...   \n",
       "4   [-146.68547873216784, -35.273931876923136, 92....   \n",
       "5   [-149.79056376648444, -34.764690085456614, 93....   \n",
       "6   [-151.56931316731388, -36.27718223357999, 94.9...   \n",
       "7   [-152.88891297935538, -36.769173225736196, 95....   \n",
       "8   [-154.18196982402736, -37.053771360193124, 96....   \n",
       "9   [-155.92347754054478, -37.392301324447665, 96....   \n",
       "10  [-156.57263382288738, -37.50160617150344, 97.2...   \n",
       "\n",
       "                                           chi_in_MHz    χr MHz      gr MHz  \n",
       "1   [-4.743896200332651, -0.15064122171714955, -1....  4.743896  149.793893  \n",
       "2   [-3.688792537026435, -0.16027905223925185, -0....  3.688793  140.369372  \n",
       "3   [-3.5564231159948405, -0.13780865504675055, -0...  3.556423  145.229648  \n",
       "4   [-3.4339388654046306, -0.13986054119450542, -0...  3.433939  146.685479  \n",
       "5   [-3.4133645521411466, -0.13022118096526072, -0...  3.413365  149.790564  \n",
       "6   [-3.3615173453262583, -0.13699457048810307, -0...  3.361517  151.569313  \n",
       "7   [-3.3484480460203634, -0.1381096261830813, -0....  3.348448  152.888913  \n",
       "8   [-3.3460100166051583, -0.1380842949746457, -0....  3.346010  154.181970  \n",
       "9   [-3.3763925120509524, -0.138951259968651, -0.8...  3.376393  155.923478  \n",
       "10  [-3.372645810154595, -0.13859898335873305, -0....  3.372646  156.572634  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2.setup.junctions = Dict(Lj=12, Cj=1)\n",
    "c2.setup.freq_readout = 7.1\n",
    "c2.setup.freq_bus = [7.5, 7.6, 7.9, 17]\n",
    "\n",
    "c2.run_lom()\n",
    "c2.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Tune_Q2\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:17AM [hfss_report_full_convergence]: Creating report for variation 0\n",
      "INFO 12:17AM [__del__]: Disconnected from Ansys HFSS\n"
     ]
    }
   ],
   "source": [
    "c2.plot_convergence()\n",
    "c2.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./new graphs/q2 fig1.png\">\n",
    "<img src=\"./new graphs/q2 fig2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Qubit (Q3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:17AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 12:17AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 12:17AM [load_ansys_project]: \tOpened Ansys Desktop v2021.1.0\n",
      "INFO 12:17AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Users/Nilay/Documents/Ansoft/\n",
      "\tProject:   Project3\n",
      "INFO 12:17AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q2 [Solution type: Q3D]\n",
      "INFO 12:17AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:17AM [connect]: \tConnected to project \"Project3\" and design \"Tune_Q2\" 😀 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c3 = LOManalysis(design, \"q3d\")\n",
    "q3d3 = c3.sim.renderer\n",
    "q3d3.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:17AM 33s WARNING [activate_ansys_design]: The design_name=Tune_Q3 was not in active project.  Designs in active project are: \n",
      "['Tune_Q2', 'Tune_Q1'].  A new design will be added to the project.  \n",
      "INFO 12:17AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q3 [Solution type: Q3D]\n",
      "WARNING 12:17AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 12:17AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 12:17AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n"
     ]
    }
   ],
   "source": [
    "transmons[2].options.pad_gap    = '40um'\n",
    "transmons[2].options.pad_width  = '353um'\n",
    "transmons[2].options.pad_height = '90um'\n",
    "gui.rebuild()\n",
    "\n",
    "q3d3.activate_ansys_design(\"Tune_Q3\", 'capacitive')\n",
    "q3d3.render_design(['Q3'], [('Q3', 'c'), ('Q3', 'a'),('Q3', 'b'),('Q3', 'd'),('Q3', 'Charge_Line')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#q3d3.save_screenshot(\"C:/Users/Nilay/Documents/GitHub/qiskit-metal-qubit-design/q3.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:17AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:17AM [analyze]: Analyzing setup Setup\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpicivxf_q.txt, C, , Setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpqpfjy_71.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpc03apk6m.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp5tw0mqwk.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp4nn9or8h.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp805_ilbf.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpytjrxdeq.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpj5p1c332.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpfcirhqva.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpxln5sy4z.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpn29s7ze8.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n",
      "INFO 12:20AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpbbol6vnn.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 11, False\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a_connector_pad_Q3</th>\n",
       "      <th>b_connector_pad_Q3</th>\n",
       "      <th>c_connector_pad_Q3</th>\n",
       "      <th>cl_metal_Q3</th>\n",
       "      <th>d_connector_pad_Q3</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q3</th>\n",
       "      <th>pad_top_Q3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a_connector_pad_Q3</th>\n",
       "      <td>52.28885</td>\n",
       "      <td>-0.41659</td>\n",
       "      <td>-0.76071</td>\n",
       "      <td>-0.01050</td>\n",
       "      <td>-0.18477</td>\n",
       "      <td>-34.97058</td>\n",
       "      <td>-13.46970</td>\n",
       "      <td>-1.52277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_connector_pad_Q3</th>\n",
       "      <td>-0.41659</td>\n",
       "      <td>52.29473</td>\n",
       "      <td>-0.18476</td>\n",
       "      <td>-0.01247</td>\n",
       "      <td>-0.76366</td>\n",
       "      <td>-34.97972</td>\n",
       "      <td>-1.51692</td>\n",
       "      <td>-13.47514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_connector_pad_Q3</th>\n",
       "      <td>-0.76071</td>\n",
       "      <td>-0.18476</td>\n",
       "      <td>52.30764</td>\n",
       "      <td>-0.05609</td>\n",
       "      <td>-0.41656</td>\n",
       "      <td>-34.93612</td>\n",
       "      <td>-13.47626</td>\n",
       "      <td>-1.52348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cl_metal_Q3</th>\n",
       "      <td>-0.01050</td>\n",
       "      <td>-0.01247</td>\n",
       "      <td>-0.05609</td>\n",
       "      <td>15.96195</td>\n",
       "      <td>-0.15577</td>\n",
       "      <td>-15.42211</td>\n",
       "      <td>-0.09945</td>\n",
       "      <td>-0.13542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d_connector_pad_Q3</th>\n",
       "      <td>-0.18477</td>\n",
       "      <td>-0.76366</td>\n",
       "      <td>-0.41656</td>\n",
       "      <td>-0.15577</td>\n",
       "      <td>52.36696</td>\n",
       "      <td>-34.90943</td>\n",
       "      <td>-1.51670</td>\n",
       "      <td>-13.47723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-34.97058</td>\n",
       "      <td>-34.97972</td>\n",
       "      <td>-34.93612</td>\n",
       "      <td>-15.42211</td>\n",
       "      <td>-34.90943</td>\n",
       "      <td>270.01646</td>\n",
       "      <td>-27.92710</td>\n",
       "      <td>-27.90836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q3</th>\n",
       "      <td>-13.46970</td>\n",
       "      <td>-1.51692</td>\n",
       "      <td>-13.47626</td>\n",
       "      <td>-0.09945</td>\n",
       "      <td>-1.51670</td>\n",
       "      <td>-27.92710</td>\n",
       "      <td>82.20132</td>\n",
       "      <td>-22.02885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q3</th>\n",
       "      <td>-1.52277</td>\n",
       "      <td>-13.47514</td>\n",
       "      <td>-1.52348</td>\n",
       "      <td>-0.13542</td>\n",
       "      <td>-13.47723</td>\n",
       "      <td>-27.90836</td>\n",
       "      <td>-22.02885</td>\n",
       "      <td>82.20864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    a_connector_pad_Q3  b_connector_pad_Q3  \\\n",
       "a_connector_pad_Q3            52.28885            -0.41659   \n",
       "b_connector_pad_Q3            -0.41659            52.29473   \n",
       "c_connector_pad_Q3            -0.76071            -0.18476   \n",
       "cl_metal_Q3                   -0.01050            -0.01247   \n",
       "d_connector_pad_Q3            -0.18477            -0.76366   \n",
       "ground_main_plane            -34.97058           -34.97972   \n",
       "pad_bot_Q3                   -13.46970            -1.51692   \n",
       "pad_top_Q3                    -1.52277           -13.47514   \n",
       "\n",
       "                    c_connector_pad_Q3  cl_metal_Q3  d_connector_pad_Q3  \\\n",
       "a_connector_pad_Q3            -0.76071     -0.01050            -0.18477   \n",
       "b_connector_pad_Q3            -0.18476     -0.01247            -0.76366   \n",
       "c_connector_pad_Q3            52.30764     -0.05609            -0.41656   \n",
       "cl_metal_Q3                   -0.05609     15.96195            -0.15577   \n",
       "d_connector_pad_Q3            -0.41656     -0.15577            52.36696   \n",
       "ground_main_plane            -34.93612    -15.42211           -34.90943   \n",
       "pad_bot_Q3                   -13.47626     -0.09945            -1.51670   \n",
       "pad_top_Q3                    -1.52348     -0.13542           -13.47723   \n",
       "\n",
       "                    ground_main_plane  pad_bot_Q3  pad_top_Q3  \n",
       "a_connector_pad_Q3          -34.97058   -13.46970    -1.52277  \n",
       "b_connector_pad_Q3          -34.97972    -1.51692   -13.47514  \n",
       "c_connector_pad_Q3          -34.93612   -13.47626    -1.52348  \n",
       "cl_metal_Q3                 -15.42211    -0.09945    -0.13542  \n",
       "d_connector_pad_Q3          -34.90943    -1.51670   -13.47723  \n",
       "ground_main_plane           270.01646   -27.92710   -27.90836  \n",
       "pad_bot_Q3                  -27.92710    82.20132   -22.02885  \n",
       "pad_top_Q3                  -27.90836   -22.02885    82.20864  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3d3.add_q3d_setup(name=\"Setup\", max_passes=15, min_converged_passes=5,percent_error=0.05 )\n",
    "q3d3.analyze_setup(\"Setup\")\n",
    "c3.sim.capacitance_matrix, c3.sim.units = q3d3.get_capacitance_matrix()\n",
    "c3.sim.capacitance_all_passes, _ = q3d3.get_capacitance_all_passes()\n",
    "c3.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 6] [7 0 1 2 3]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.216237 [GHz]\n",
      "EC 278.739167 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -320.804891 [MHz]\n",
      "dispersion 13.393934 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 69.492308 [fF]\n",
      "T1 52.051933 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 -10.967014 [fF]\n",
      "gbus1_in_MHz -158.622753 [MHz]\n",
      "χ_bus1 -3.327696 [MHz]\n",
      "1/T1bus1 2938.896308 [Hz]\n",
      "T1bus1 54.154664 [us]\n",
      "\n",
      "tCqbus2 -2.739456 [fF]\n",
      "gbus2_in_MHz -41.972589 [MHz]\n",
      "χ_bus2 -0.169213 [MHz]\n",
      "1/T1bus2 14.345132 [Hz]\n",
      "T1bus2 11094.700148 [us]\n",
      "\n",
      "tCqbus3 6.567630 [fF]\n",
      "gbus3_in_MHz 99.205687 [MHz]\n",
      "χ_bus3 -1.018883 [MHz]\n",
      "1/T1bus3 87.219614 [Hz]\n",
      "T1bus3 1824.760924 [us]\n",
      "\n",
      "tCqbus4 -2.752197 [fF]\n",
      "gbus4_in_MHz -42.710318 [MHz]\n",
      "χ_bus4 -0.163039 [MHz]\n",
      "1/T1bus4 13.691739 [Hz]\n",
      "T1bus4 11624.158657 [us]\n",
      "\n",
      "tCqbus5 3.338807 [fF]\n",
      "gbus5_in_MHz 113.299165 [MHz]\n",
      "χ_bus5 -0.074674 [MHz]\n",
      "1/T1bus5 3.465516 [Hz]\n",
      "T1bus5 45925.317315 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 36.725642 [MHz]\n",
      "gbus1_3 64.864683 [MHz]\n",
      "gbus1_4 37.168399 [MHz]\n",
      "gbus1_5 23.860431 [MHz]\n",
      "gbus2_3 26.012718 [MHz]\n",
      "gbus2_4 36.262180 [MHz]\n",
      "gbus2_5 23.932435 [MHz]\n",
      "gbus3_4 25.553806 [MHz]\n",
      "gbus3_5 17.749633 [MHz]\n",
      "gbus4_5 24.562246 [MHz]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.539726</td>\n",
       "      <td>316.924414</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-369.200269</td>\n",
       "      <td>43.778287</td>\n",
       "      <td>[-150.56006723862984, -41.66407627029326, 98.7...</td>\n",
       "      <td>[-4.569922634144913, -0.24209425722697753, -1....</td>\n",
       "      <td>4.569923</td>\n",
       "      <td>150.560067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.441813</td>\n",
       "      <td>305.070169</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-354.064844</td>\n",
       "      <td>31.055877</td>\n",
       "      <td>[-141.15600184721654, -41.87295870673714, 91.3...</td>\n",
       "      <td>[-3.5217086153558594, -0.2178653543879288, -1....</td>\n",
       "      <td>3.521709</td>\n",
       "      <td>141.156002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.379256</td>\n",
       "      <td>297.631756</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-344.619022</td>\n",
       "      <td>24.772985</td>\n",
       "      <td>[-147.4667597469534, -36.56814923539504, 92.12...</td>\n",
       "      <td>[-3.5405271489164143, -0.15453535009288596, -1...</td>\n",
       "      <td>3.540527</td>\n",
       "      <td>147.466760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.324032</td>\n",
       "      <td>291.152522</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-336.4234</td>\n",
       "      <td>20.200849</td>\n",
       "      <td>[-150.04578623955163, -38.17194289513515, 94.1...</td>\n",
       "      <td>[-3.412952769500439, -0.15806195362186848, -1....</td>\n",
       "      <td>3.412953</td>\n",
       "      <td>150.045786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.29334</td>\n",
       "      <td>287.586754</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-331.92577</td>\n",
       "      <td>18.001575</td>\n",
       "      <td>[-152.2316819527803, -38.92889738159924, 95.16...</td>\n",
       "      <td>[-3.3779723670046087, -0.15875725133700144, -1...</td>\n",
       "      <td>3.377972</td>\n",
       "      <td>152.231682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.264906</td>\n",
       "      <td>284.305594</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-327.795091</td>\n",
       "      <td>16.158585</td>\n",
       "      <td>[-154.52958109543945, -39.700480370517546, 96....</td>\n",
       "      <td>[-3.357392547034651, -0.15989033929850932, -1....</td>\n",
       "      <td>3.357393</td>\n",
       "      <td>154.529581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.249876</td>\n",
       "      <td>282.579919</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-325.625684</td>\n",
       "      <td>15.254592</td>\n",
       "      <td>[-155.48882879319794, -40.40470335703269, 97.6...</td>\n",
       "      <td>[-3.3353397330404313, -0.16283410273255786, -1...</td>\n",
       "      <td>3.335340</td>\n",
       "      <td>155.488829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.235086</td>\n",
       "      <td>280.88754</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-323.500178</td>\n",
       "      <td>14.409638</td>\n",
       "      <td>[-156.1731370924212, -41.435762419263085, 98.0...</td>\n",
       "      <td>[-3.3027628268381903, -0.16842988427747, -1.01...</td>\n",
       "      <td>3.302763</td>\n",
       "      <td>156.173137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.224354</td>\n",
       "      <td>279.663234</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-321.963799</td>\n",
       "      <td>13.82318</td>\n",
       "      <td>[-157.94417944216144, -41.563565095092585, 98....</td>\n",
       "      <td>[-3.3329550593146364, -0.16744505356894196, -1...</td>\n",
       "      <td>3.332955</td>\n",
       "      <td>157.944179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.216237</td>\n",
       "      <td>278.739167</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-320.804891</td>\n",
       "      <td>13.393934</td>\n",
       "      <td>[-158.6227533874381, -41.97258914366891, 99.20...</td>\n",
       "      <td>[-3.3276959468337624, -0.16921347345633767, -1...</td>\n",
       "      <td>3.327696</td>\n",
       "      <td>158.622753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fQ          EC       EJ       alpha dispersion  \\\n",
       "1   5.539726  316.924414  13.6163 -369.200269  43.778287   \n",
       "2   5.441813  305.070169  13.6163 -354.064844  31.055877   \n",
       "3   5.379256  297.631756  13.6163 -344.619022  24.772985   \n",
       "4   5.324032  291.152522  13.6163   -336.4234  20.200849   \n",
       "5    5.29334  287.586754  13.6163  -331.92577  18.001575   \n",
       "6   5.264906  284.305594  13.6163 -327.795091  16.158585   \n",
       "7   5.249876  282.579919  13.6163 -325.625684  15.254592   \n",
       "8   5.235086   280.88754  13.6163 -323.500178  14.409638   \n",
       "9   5.224354  279.663234  13.6163 -321.963799   13.82318   \n",
       "10  5.216237  278.739167  13.6163 -320.804891  13.393934   \n",
       "\n",
       "                                                 gbus  \\\n",
       "1   [-150.56006723862984, -41.66407627029326, 98.7...   \n",
       "2   [-141.15600184721654, -41.87295870673714, 91.3...   \n",
       "3   [-147.4667597469534, -36.56814923539504, 92.12...   \n",
       "4   [-150.04578623955163, -38.17194289513515, 94.1...   \n",
       "5   [-152.2316819527803, -38.92889738159924, 95.16...   \n",
       "6   [-154.52958109543945, -39.700480370517546, 96....   \n",
       "7   [-155.48882879319794, -40.40470335703269, 97.6...   \n",
       "8   [-156.1731370924212, -41.435762419263085, 98.0...   \n",
       "9   [-157.94417944216144, -41.563565095092585, 98....   \n",
       "10  [-158.6227533874381, -41.97258914366891, 99.20...   \n",
       "\n",
       "                                           chi_in_MHz    χr MHz      gr MHz  \n",
       "1   [-4.569922634144913, -0.24209425722697753, -1....  4.569923  150.560067  \n",
       "2   [-3.5217086153558594, -0.2178653543879288, -1....  3.521709  141.156002  \n",
       "3   [-3.5405271489164143, -0.15453535009288596, -1...  3.540527  147.466760  \n",
       "4   [-3.412952769500439, -0.15806195362186848, -1....  3.412953  150.045786  \n",
       "5   [-3.3779723670046087, -0.15875725133700144, -1...  3.377972  152.231682  \n",
       "6   [-3.357392547034651, -0.15989033929850932, -1....  3.357393  154.529581  \n",
       "7   [-3.3353397330404313, -0.16283410273255786, -1...  3.335340  155.488829  \n",
       "8   [-3.3027628268381903, -0.16842988427747, -1.01...  3.302763  156.173137  \n",
       "9   [-3.3329550593146364, -0.16744505356894196, -1...  3.332955  157.944179  \n",
       "10  [-3.3276959468337624, -0.16921347345633767, -1...  3.327696  158.622753  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c3.setup.junctions = Dict(Lj=12, Cj=1)\n",
    "c3.setup.freq_readout = 7.3\n",
    "c3.setup.freq_bus = [7.7, 7.6, 7.8, 17]\n",
    "\n",
    "c3.run_lom()\n",
    "c3.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Tune_Q3\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:20AM [hfss_report_full_convergence]: Creating report for variation 0\n"
     ]
    }
   ],
   "source": [
    "c3.plot_convergence()\n",
    "c3.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./new graphs/q3 fig1.png\">\n",
    "<img src=\"./new graphs/q3 fig2.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Qubit (Q4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:20AM [__del__]: Disconnected from Ansys HFSS\n",
      "INFO 12:20AM [connect_project]: Connecting to Ansys Desktop API...\n",
      "INFO 12:20AM [load_ansys_project]: \tOpened Ansys App\n",
      "INFO 12:20AM [load_ansys_project]: \tOpened Ansys Desktop v2021.1.0\n",
      "INFO 12:20AM [load_ansys_project]: \tOpened Ansys Project\n",
      "\tFolder:    C:/Users/Nilay/Documents/Ansoft/\n",
      "\tProject:   Project3\n",
      "INFO 12:20AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q3 [Solution type: Q3D]\n",
      "INFO 12:20AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:20AM [connect]: \tConnected to project \"Project3\" and design \"Tune_Q3\" 😀 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_metal.analyses.quantization import LOManalysis\n",
    "c4 = LOManalysis(design, \"q3d\")\n",
    "q3d4 = c4.sim.renderer\n",
    "q3d4.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "12:20AM 23s WARNING [activate_ansys_design]: The design_name=Tune_Q4 was not in active project.  Designs in active project are: \n",
      "['Tune_Q2', 'Tune_Q3', 'Tune_Q1'].  A new design will be added to the project.  \n",
      "INFO 12:20AM [connect_design]: \tOpened active design\n",
      "\tDesign:    Tune_Q4 [Solution type: Q3D]\n",
      "WARNING 12:20AM [connect_setup]: \tNo design setup detected.\n",
      "WARNING 12:20AM [connect_setup]: \tCreating Q3D default setup.\n",
      "INFO 12:20AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n"
     ]
    }
   ],
   "source": [
    "transmons[3].options.pad_gap    = '40um'\n",
    "transmons[3].options.pad_width  = '344um'\n",
    "transmons[3].options.pad_height = '90um'\n",
    "gui.rebuild()\n",
    "\n",
    "q3d4.activate_ansys_design(\"Tune_Q4\", 'capacitive')\n",
    "q3d4.render_design(['Q4'], [('Q4', 'c'), ('Q4', 'a'),('Q4', 'b'),('Q4', 'Charge_Line')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#q3d4.save_screenshot(\"C:/Users/Nilay/Documents/GitHub/qiskit-metal-qubit-design/q4.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:20AM [get_setup]: \tOpened setup `Setup`  (<class 'pyEPR.ansys.AnsysQ3DSetup'>)\n",
      "INFO 12:20AM [analyze]: Analyzing setup Setup\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp1lrjz1eq.txt, C, , Setup:LastAdaptive, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpz19iwj6m.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 1, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpc1ovb0qt.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 2, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp8l8yd8k8.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 3, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmphguovvrd.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 4, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpohoxg1nw.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 5, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpcwo2eudh.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 6, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpzqpryjrx.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 7, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpojskkl73.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 8, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmpllye5vi_.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 9, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmps870mh69.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 10, False\n",
      "INFO 12:22AM [get_matrix]: Exporting matrix data to (C:\\Users\\Nilay\\AppData\\Local\\Temp\\tmp6kw1v48x.txt, C, , Setup:AdaptivePass, \"Original\", \"ohm\", \"nH\", \"fF\", \"mSie\", 5000000000, Maxwell, 11, False\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a_connector_pad_Q4</th>\n",
       "      <th>b_connector_pad_Q4</th>\n",
       "      <th>c_connector_pad_Q4</th>\n",
       "      <th>cl_metal_Q4</th>\n",
       "      <th>ground_main_plane</th>\n",
       "      <th>pad_bot_Q4</th>\n",
       "      <th>pad_top_Q4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a_connector_pad_Q4</th>\n",
       "      <td>52.53668</td>\n",
       "      <td>-0.43711</td>\n",
       "      <td>-0.84400</td>\n",
       "      <td>-0.01147</td>\n",
       "      <td>-35.21673</td>\n",
       "      <td>-13.45867</td>\n",
       "      <td>-1.60021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b_connector_pad_Q4</th>\n",
       "      <td>-0.43711</td>\n",
       "      <td>52.43753</td>\n",
       "      <td>-0.20207</td>\n",
       "      <td>-0.01500</td>\n",
       "      <td>-35.42431</td>\n",
       "      <td>-1.55821</td>\n",
       "      <td>-13.82001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c_connector_pad_Q4</th>\n",
       "      <td>-0.84400</td>\n",
       "      <td>-0.20207</td>\n",
       "      <td>52.48310</td>\n",
       "      <td>-0.05860</td>\n",
       "      <td>-35.26452</td>\n",
       "      <td>-13.46672</td>\n",
       "      <td>-1.66976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cl_metal_Q4</th>\n",
       "      <td>-0.01147</td>\n",
       "      <td>-0.01500</td>\n",
       "      <td>-0.05860</td>\n",
       "      <td>16.01220</td>\n",
       "      <td>-15.58622</td>\n",
       "      <td>-0.10213</td>\n",
       "      <td>-0.16477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground_main_plane</th>\n",
       "      <td>-35.21673</td>\n",
       "      <td>-35.42431</td>\n",
       "      <td>-35.26452</td>\n",
       "      <td>-15.58622</td>\n",
       "      <td>241.52243</td>\n",
       "      <td>-27.95310</td>\n",
       "      <td>-32.50450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_bot_Q4</th>\n",
       "      <td>-13.45867</td>\n",
       "      <td>-1.55821</td>\n",
       "      <td>-13.46672</td>\n",
       "      <td>-0.10213</td>\n",
       "      <td>-27.95310</td>\n",
       "      <td>80.66906</td>\n",
       "      <td>-21.97395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pad_top_Q4</th>\n",
       "      <td>-1.60021</td>\n",
       "      <td>-13.82001</td>\n",
       "      <td>-1.66976</td>\n",
       "      <td>-0.16477</td>\n",
       "      <td>-32.50450</td>\n",
       "      <td>-21.97395</td>\n",
       "      <td>74.13410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    a_connector_pad_Q4  b_connector_pad_Q4  \\\n",
       "a_connector_pad_Q4            52.53668            -0.43711   \n",
       "b_connector_pad_Q4            -0.43711            52.43753   \n",
       "c_connector_pad_Q4            -0.84400            -0.20207   \n",
       "cl_metal_Q4                   -0.01147            -0.01500   \n",
       "ground_main_plane            -35.21673           -35.42431   \n",
       "pad_bot_Q4                   -13.45867            -1.55821   \n",
       "pad_top_Q4                    -1.60021           -13.82001   \n",
       "\n",
       "                    c_connector_pad_Q4  cl_metal_Q4  ground_main_plane  \\\n",
       "a_connector_pad_Q4            -0.84400     -0.01147          -35.21673   \n",
       "b_connector_pad_Q4            -0.20207     -0.01500          -35.42431   \n",
       "c_connector_pad_Q4            52.48310     -0.05860          -35.26452   \n",
       "cl_metal_Q4                   -0.05860     16.01220          -15.58622   \n",
       "ground_main_plane            -35.26452    -15.58622          241.52243   \n",
       "pad_bot_Q4                   -13.46672     -0.10213          -27.95310   \n",
       "pad_top_Q4                    -1.66976     -0.16477          -32.50450   \n",
       "\n",
       "                    pad_bot_Q4  pad_top_Q4  \n",
       "a_connector_pad_Q4   -13.45867    -1.60021  \n",
       "b_connector_pad_Q4    -1.55821   -13.82001  \n",
       "c_connector_pad_Q4   -13.46672    -1.66976  \n",
       "cl_metal_Q4           -0.10213    -0.16477  \n",
       "ground_main_plane    -27.95310   -32.50450  \n",
       "pad_bot_Q4            80.66906   -21.97395  \n",
       "pad_top_Q4           -21.97395    74.13410  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3d4.add_q3d_setup(name=\"Setup\", max_passes=15, min_converged_passes=5,percent_error=0.05 )\n",
    "q3d4.analyze_setup(\"Setup\")\n",
    "c4.sim.capacitance_matrix, c4.sim.units = q3d4.get_capacitance_matrix()\n",
    "c4.sim.capacitance_all_passes, _ = q3d4.get_capacitance_all_passes()\n",
    "c4.sim.capacitance_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5] [6 0 1 2]\n",
      "Predicted Values\n",
      "\n",
      "Transmon Properties\n",
      "f_Q 5.305863 [GHz]\n",
      "EC 289.038656 [MHz]\n",
      "EJ 13.616300 [GHz]\n",
      "alpha -333.756017 [MHz]\n",
      "dispersion 18.871532 [KHz]\n",
      "Lq 11.995161 [nH]\n",
      "Cq 67.016047 [fF]\n",
      "T1 77.033774 [us]\n",
      "\n",
      "**Coupling Properties**\n",
      "\n",
      "tCqbus1 -8.508622 [fF]\n",
      "gbus1_in_MHz -128.182143 [MHz]\n",
      "χ_bus1 -2.226916 [MHz]\n",
      "1/T1bus1 1933.928371 [Hz]\n",
      "T1bus1 82.296193 [us]\n",
      "\n",
      "tCqbus2 -1.427671 [fF]\n",
      "gbus2_in_MHz -23.075135 [MHz]\n",
      "χ_bus2 -0.048886 [MHz]\n",
      "1/T1bus2 4.036160 [Hz]\n",
      "T1bus2 39432.272554 [us]\n",
      "\n",
      "tCqbus3 7.582684 [fF]\n",
      "gbus3_in_MHz 119.293223 [MHz]\n",
      "χ_bus3 -1.512139 [MHz]\n",
      "1/T1bus3 127.267655 [Hz]\n",
      "T1bus3 1250.552960 [us]\n",
      "\n",
      "tCqbus4 -1.421919 [fF]\n",
      "gbus4_in_MHz -43.077194 [MHz]\n",
      "χ_bus4 -0.015796 [MHz]\n",
      "1/T1bus4 0.808904 [Hz]\n",
      "T1bus4 196753.824935 [us]\n",
      "Bus-Bus Couplings\n",
      "gbus1_2 47.011474 [MHz]\n",
      "gbus1_3 74.068645 [MHz]\n",
      "gbus1_4 85.260354 [MHz]\n",
      "gbus2_3 31.477475 [MHz]\n",
      "gbus2_4 78.847534 [MHz]\n",
      "gbus3_4 55.376596 [MHz]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fQ</th>\n",
       "      <th>EC</th>\n",
       "      <th>EJ</th>\n",
       "      <th>alpha</th>\n",
       "      <th>dispersion</th>\n",
       "      <th>gbus</th>\n",
       "      <th>chi_in_MHz</th>\n",
       "      <th>χr MHz</th>\n",
       "      <th>gr MHz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.627757</td>\n",
       "      <td>327.80476</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-383.181661</td>\n",
       "      <td>58.99485</td>\n",
       "      <td>[-119.77420300527439, -25.087269512410188, 116...</td>\n",
       "      <td>[-2.944834740082875, -0.0826503810720162, -2.1...</td>\n",
       "      <td>2.944835</td>\n",
       "      <td>119.774203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.55486</td>\n",
       "      <td>318.779823</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-371.578418</td>\n",
       "      <td>46.113553</td>\n",
       "      <td>[-112.66562600887536, -24.025211679261034, 109...</td>\n",
       "      <td>[-2.3642075575022266, -0.06976567097151155, -1...</td>\n",
       "      <td>2.364208</td>\n",
       "      <td>112.665626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.485216</td>\n",
       "      <td>310.292866</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-360.720685</td>\n",
       "      <td>36.216952</td>\n",
       "      <td>[-117.81696732691965, -19.62719918147925, 110....</td>\n",
       "      <td>[-2.360418765420548, -0.043060656403596284, -1...</td>\n",
       "      <td>2.360419</td>\n",
       "      <td>117.816967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.42513</td>\n",
       "      <td>303.076174</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-351.528846</td>\n",
       "      <td>29.254315</td>\n",
       "      <td>[-118.47904359250234, -21.125951413069995, 112...</td>\n",
       "      <td>[-2.209775478814396, -0.04667283547413458, -1....</td>\n",
       "      <td>2.209775</td>\n",
       "      <td>118.479044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.392492</td>\n",
       "      <td>299.196885</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-346.603249</td>\n",
       "      <td>25.998157</td>\n",
       "      <td>[-122.78033906316182, -20.10970797766194, 114....</td>\n",
       "      <td>[-2.2768765724955595, -0.04079972381893022, -1...</td>\n",
       "      <td>2.276877</td>\n",
       "      <td>122.780339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.358756</td>\n",
       "      <td>295.217076</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-341.561185</td>\n",
       "      <td>22.977493</td>\n",
       "      <td>[-122.71178143724947, -22.426190217448756, 116...</td>\n",
       "      <td>[-2.179817730145687, -0.04890333053900894, -1....</td>\n",
       "      <td>2.179818</td>\n",
       "      <td>122.711781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.34162</td>\n",
       "      <td>293.207225</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-339.019172</td>\n",
       "      <td>21.567142</td>\n",
       "      <td>[-125.6021479911834, -21.87845870358315, 117.1...</td>\n",
       "      <td>[-2.2353040556112056, -0.04568351039706439, -1...</td>\n",
       "      <td>2.235304</td>\n",
       "      <td>125.602148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.325741</td>\n",
       "      <td>291.351811</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-336.675037</td>\n",
       "      <td>20.330113</td>\n",
       "      <td>[-126.15762040521864, -22.834683162132652, 118...</td>\n",
       "      <td>[-2.210961151456409, -0.04891357903424267, -1....</td>\n",
       "      <td>2.210961</td>\n",
       "      <td>126.157620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.315347</td>\n",
       "      <td>290.141022</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-335.146643</td>\n",
       "      <td>19.555313</td>\n",
       "      <td>[-126.94236974445988, -22.749819191156572, 118...</td>\n",
       "      <td>[-2.209853879832585, -0.04800712754752998, -1....</td>\n",
       "      <td>2.209854</td>\n",
       "      <td>126.942370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.305863</td>\n",
       "      <td>289.038656</td>\n",
       "      <td>13.6163</td>\n",
       "      <td>-333.756017</td>\n",
       "      <td>18.871532</td>\n",
       "      <td>[-128.182143325968, -23.07513527293466, 119.29...</td>\n",
       "      <td>[-2.2269157762725937, -0.048885899554632885, -...</td>\n",
       "      <td>2.226916</td>\n",
       "      <td>128.182143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          fQ          EC       EJ       alpha dispersion  \\\n",
       "1   5.627757   327.80476  13.6163 -383.181661   58.99485   \n",
       "2    5.55486  318.779823  13.6163 -371.578418  46.113553   \n",
       "3   5.485216  310.292866  13.6163 -360.720685  36.216952   \n",
       "4    5.42513  303.076174  13.6163 -351.528846  29.254315   \n",
       "5   5.392492  299.196885  13.6163 -346.603249  25.998157   \n",
       "6   5.358756  295.217076  13.6163 -341.561185  22.977493   \n",
       "7    5.34162  293.207225  13.6163 -339.019172  21.567142   \n",
       "8   5.325741  291.351811  13.6163 -336.675037  20.330113   \n",
       "9   5.315347  290.141022  13.6163 -335.146643  19.555313   \n",
       "10  5.305863  289.038656  13.6163 -333.756017  18.871532   \n",
       "\n",
       "                                                 gbus  \\\n",
       "1   [-119.77420300527439, -25.087269512410188, 116...   \n",
       "2   [-112.66562600887536, -24.025211679261034, 109...   \n",
       "3   [-117.81696732691965, -19.62719918147925, 110....   \n",
       "4   [-118.47904359250234, -21.125951413069995, 112...   \n",
       "5   [-122.78033906316182, -20.10970797766194, 114....   \n",
       "6   [-122.71178143724947, -22.426190217448756, 116...   \n",
       "7   [-125.6021479911834, -21.87845870358315, 117.1...   \n",
       "8   [-126.15762040521864, -22.834683162132652, 118...   \n",
       "9   [-126.94236974445988, -22.749819191156572, 118...   \n",
       "10  [-128.182143325968, -23.07513527293466, 119.29...   \n",
       "\n",
       "                                           chi_in_MHz    χr MHz      gr MHz  \n",
       "1   [-2.944834740082875, -0.0826503810720162, -2.1...  2.944835  119.774203  \n",
       "2   [-2.3642075575022266, -0.06976567097151155, -1...  2.364208  112.665626  \n",
       "3   [-2.360418765420548, -0.043060656403596284, -1...  2.360419  117.816967  \n",
       "4   [-2.209775478814396, -0.04667283547413458, -1....  2.209775  118.479044  \n",
       "5   [-2.2768765724955595, -0.04079972381893022, -1...  2.276877  122.780339  \n",
       "6   [-2.179817730145687, -0.04890333053900894, -1....  2.179818  122.711781  \n",
       "7   [-2.2353040556112056, -0.04568351039706439, -1...  2.235304  125.602148  \n",
       "8   [-2.210961151456409, -0.04891357903424267, -1....  2.210961  126.157620  \n",
       "9   [-2.209853879832585, -0.04800712754752998, -1....  2.209854  126.942370  \n",
       "10  [-2.2269157762725937, -0.048885899554632885, -...  2.226916  128.182143  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c4.setup.junctions = Dict(Lj=12, Cj=1)\n",
    "c4.setup.freq_readout = 7.4\n",
    "c4.setup.freq_bus = [7.9, 7.7, 15]\n",
    "\n",
    "c4.run_lom()\n",
    "c4.lumped_oscillator_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Design \"Tune_Q4\" info:\n",
      "\t# eigenmodes    0\n",
      "\t# variations    1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO 12:22AM [hfss_report_full_convergence]: Creating report for variation 0\n"
     ]
    }
   ],
   "source": [
    "c4.plot_convergence()\n",
    "c4.plot_convergence_chi()"
   ]
  },
  {
   "cell_type": "markdown",
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